Systems and methods for personalized cardiovascular analyses

ABSTRACT

Systems and methods for performing personalized cardiovascular analyses are provided. A method includes building, using a modeling and simulation computing device, a patient-specific model, storing, using the modeling and simulation computing device, the patient-specific model in a database, receiving, at the modeling and simulation computing device, remote monitoring data from at least one remote monitoring data source, and receiving, at the modeling and simulation computing device, clinical data from at least one clinical data source. The method further includes updating, using the modeling and simulation computing device, the patient-specific model using the remote monitoring data and the clinical data, performing, using the modeling and simulation computing device, at least one simulation on the updated patient-specific model, and outputting, from the modeling and simulation computing device, at least one output based on the at least one simulation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to provisional application Ser. No.62/951,312, filed Dec. 20, 2019, which is incorporated herein byreference in its entirety.

FIELD OF THE DISCLOSURE

This disclosure relates to monitoring patients with cardiovascularissues, and more particularly, this disclosure relates to executingpatient-specific cardiovascular analyses.

BACKGROUND

Ventricular assist systems (VASs) may include ventricular assist devices(VADs), such as implantable blood pumps used for both short-term (i.e.,days, months) and long-term (i.e., years or a lifetime) applicationswhere a patient's heart is incapable of providing adequate circulation,commonly referred to as heart failure or congestive heart failure. Apatient suffering from heart failure may use a VAS while awaiting aheart transplant or as a long-term destination therapy. In anotherexample, a patient may use a VAS while recovering from heart surgery.Thus, a VAS can supplement a weak heart (i.e., partial support) or caneffectively replace the natural heart's function. VASs can be implantedin the patient's body and powered by an electrical power source insideor outside the patient's body.

Understanding and managing the hemodynamic state of a heart failurepatient may be relatively challenging. Further, additional complexityarises for VAD patients with one or more underlying conditions, such asright ventricle (RV) dysfunction, valve disorders, arrhythmias, etc.

One focus of next-generation VAD development is adding systemenhancements to assist physicians in optimizing therapy to improveadverse event profiles and overall patient quality of life. Exampleenhancements include using improved sensing abilities (e.g., flowwaveform sensing, pressure waveform sensing, accelerometers, etc.) inconjunction with closed loop control algorithms (e.g., pulsing andphysiologic control). These enhancements may be utilized in so-called“Smart VADs”.

However, with these new capabilities comes an increase in complexity andsophistication of patient management, particularly if a patient hasadditional devices, such as pacemakers or pulmonary artery (PA) pressuresensors. Thus, there is a need to educate and train physicians regardinghow to manage complex patient/device system interactions, and tounderstand the meaning of new diagnostic tools as they come on line.Further, there is a need to simplify and consolidate cardiac assistdevice interfaces, to leverage remote monitoring to recordpatient/device data outside the clinic for extended periods, and todevelop algorithms that can process and interpret device data tofacilitate optimizing patient treatment. Accordingly, there is a needfor a unified remote monitoring infrastructure.

BRIEF SUMMARY OF THE DISCLOSURE

In one embodiment, the present disclosure is directed to a method forperforming personalized cardiovascular analyses. The method includesbuilding, using a modeling and simulation computing device, apatient-specific model, storing, using the modeling and simulationcomputing device, the patient-specific model in a database, receiving,at the modeling and simulation computing device, remote monitoring datafrom at least one remote monitoring data source, and receiving, at themodeling and simulation computing device, clinical data from at leastone clinical data source. The method further includes updating, usingthe modeling and simulation computing device, the patient-specific modelusing the remote monitoring data and the clinical data, performing,using the modeling and simulation computing device, at least onesimulation on the updated patient-specific model, and outputting, fromthe modeling and simulation computing device, at least one output basedon the at least one simulation.

In another embodiment, the present disclosure is directed to a computingdevice for performing personalized cardiovascular analyses. Thecomputing device includes a memory device and a processorcommunicatively coupled to the memory device. The processor isconfigured to build a patient-specific model, store the patient-specificmodel in the memory device, receive remote monitoring data from at leastone remote monitoring data source, receive clinical data from at leastone clinical data source, update the patient-specific model using theremote monitoring data and the clinical data, perform at least onesimulation on the updated patient-specific model, and output an outputbased on the at least one simulation.

In yet another embodiment, the present disclosure is directed tonon-transitory computer-readable media having computer-executableinstructions thereon. When executed by a processor of a computingdevice, the instructions cause the processor of the computing device tobuild a patient-specific model, store the patient-specific model in adatabase, receive remote monitoring data from at least one remotemonitoring data source, receive clinical data from at least one clinicaldata source, update the patient-specific model using the remotemonitoring data and the clinical data, perform at least one simulationon the updated patient-specific model, and output an output based on theat least one simulation.

The foregoing and other aspects, features, details, utilities andadvantages of the present disclosure will be apparent from reading thefollowing description and claims, and from reviewing the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of one embodiment of a remote monitoringand simulation system.

FIG. 2 is a block diagram of one embodiment of a computing device thatmay be used to implement the systems and methods described herein.

FIG. 3 is a block diagram of a method of performing personalizedcardiovascular analyses.

Corresponding reference characters indicate corresponding partsthroughout the several views of the drawings.

DETAILED DESCRIPTION OF THE DISCLOSURE

The disclosure provides systems and methods for performing personalizedcardiovascular analyses. A method includes building, using a modelingand simulation computing device, a patient-specific model, storing,using the modeling and simulation computing device, the patient-specificmodel in a database, receiving, at the modeling and simulation computingdevice, remote monitoring data from at least one remote monitoring datasource, and receiving, at the modeling and simulation computing device,clinical data from at least one clinical data source. The method furtherincludes updating, using the modeling and simulation computing device,the patient-specific model using the remote monitoring data and theclinical data, performing, using the modeling and simulation computingdevice, at least one simulation on the updated patient-specific model,and outputting, from the modeling and simulation computing device, atleast one output based on the at least one simulation.

The embodiments described herein provide a cloud-based remote monitoringand simulation system that stores and maintains a patient-specificnumerical cardiovascular model. The system uses machine learning andoptimization techniques to continually update simulation parameters forthe model using actual patient data collected from clinical data sourcesand remote monitoring data sources. The more data collected by thesystem, the more accurate the model. Because the model replicates aparticular patient, the model may also be referred to as a “digitalclone” of the particular patient.

Referring now to the drawings wherein like reference numerals are usedto identify identical components in the various views, FIG. 1illustrates one embodiment of a remote monitoring and simulation system100. System 100 includes a modeling and simulation computing device 102.In the exemplary embodiment, modeling and simulation computing device102 is a cloud-based server system. Alternatively, modeling andsimulation computing device 102 may be any computing device suitable forimplementing the systems and methods described herein.

Modeling and simulation computing device 102 is communicatively coupledto a plurality of remote monitoring data sources 104 and a plurality ofclinical data sources 106. To build and update a patient-specific modelfor a particular patient, modeling and simulation computing device 102collects remote monitoring data associated with the patient from remotemonitoring data sources 104 and clinical data associated with thepatient from clinical data sources 106, as described herein. In someembodiments, the data may be time-stamped as it is received by modelingand simulation computing device 102.

Remote monitoring data sources 104 associated with the patient mayinclude, for example, a patient monitoring device 110, a heart failuremonitor 112, a cardiac resynchronization therapy (CRT) device 114, and aVAD 116. In some embodiments, patient monitoring device 110 initiallycollects remote data from other devices (e.g., heart failure monitor112, CRT device 114, and VAD 116) and relays that remote monitoring datato modeling and simulation computing device 102. Alternatively, eachdevice may independently transmit remote monitoring data to modeling andsimulation computing device 102.

Remote monitoring data collected from remote monitoring data sources 104may include patient hemodynamic data and device status data. Forexample, collected data may include pump flow waveforms, leftventricular (LV) pressure wave forms, and aortic pressure wave forms(e.g., from VAD 116). Also, collected remote monitoring data mayadditionally or alternatively include pulmonary artery (PA) pressurewaveforms (e.g., from heart failure monitor 112), CRT data (e.g., fromCRT device 114), and implantable cardiac monitor data. Those of skill inthe art will appreciate that other types of remote monitoring data mayalso be collected.

Clinical data sources 106 may include, for example, a physiciancomputing device, an electronic medical record system, etc. Further,clinical data for the patient may include, for example, in clinicmeasurements, such as right catheter measurements, echocardiogram data,blood pressure measurements, etc.

FIG. 2 illustrates one embodiment of a computing device 200 that may beused to implement the systems and methods described herein. Computingdevice 200 may be used, for example, to implement modeling andsimulation computing device 102 (shown in FIG. 1 ). Computing device 200includes at least one memory device 210 and a processor 215 that iscoupled to memory device 210 for executing instructions. In someembodiments, executable instructions are stored in memory device 210. Inthis embodiment, computing device 200 performs one or more operationsdescribed herein by programming processor 215. For example, processor215 may be programmed by encoding an operation as one or more executableinstructions and by providing the executable instructions in memorydevice 210.

Processor 215 may include one or more processing units (e.g., in amulti-core configuration). Further, processor 215 may be implementedusing one or more heterogeneous processor systems in which a mainprocessor is present with secondary processors on a single chip. Inanother illustrative example, processor 215 may be a symmetricmulti-processor system containing multiple processors of the same type.Further, processor 215 may be implemented using any suitableprogrammable circuit including one or more systems and microcontrollers,microprocessors, reduced instruction set circuits (RISC), applicationspecific integrated circuits (ASIC), programmable logic circuits, fieldprogrammable gate arrays (FPGA), and any other circuit capable ofexecuting the functions described herein.

In this embodiment, memory device 210 is one or more devices that enableinformation such as executable instructions and/or other data to bestored and retrieved. Memory device 210 may include one or more computerreadable media, such as, without limitation, dynamic random accessmemory (DRAM), static random access memory (SRAM), a solid state disk,and/or a hard disk. Memory device 210 may be configured to store,without limitation, application source code, application object code,source code portions of interest, object code portions of interest,configuration data, execution events and/or any other type of data.

In this embodiment, computing device 200 includes a presentationinterface 220 that is coupled to processor 215. Presentation interface220 presents information to a user 225. For example, presentationinterface 220 may include a display adapter (not shown) that may becoupled to a display device, such as a cathode ray tube, a liquidcrystal display (LCD), an organic LED (OLED) display, and/or an“electronic ink” display. In some embodiments, presentation interface220 includes one or more display devices. Input signals and/or filteredsignals processed using the embodiments described herein may bedisplayed on presentation interface 220.

In this embodiment, computing device 200 includes a user input interface235. User input interface 235 is coupled to processor 215 and receivesinput from user 225. User input interface 235 may include, for example,a keyboard, a pointing device, a mouse, a stylus, a touch sensitivepanel (e.g., a touch pad or a touch screen), a gyroscope, anaccelerometer, a position detector, and/or an audio user inputinterface. A single component, such as a touch screen, may function asboth a display device of presentation interface 220 and user inputinterface 235.

Computing device 200, in this embodiment, includes a communicationinterface 240 coupled to processor 215. Communication interface 240communicates with one or more remote devices. To communicate with remotedevices, communication interface 240 may include, for example, a wirednetwork adapter, a wireless network adapter, and/or a mobiletelecommunications adapter.

Referring back to FIG. 1 , using the remote monitoring data collectedfrom remote monitoring data sources 104 and the clinical data connectedfrom clinical data sources 106, modeling and simulation computing device102 builds and updates a patient-specific model. The patient-specificmodel may be stored, for example, in memory device 210 (shown in FIG. 2).

Subsequently, modeling and simulation computing device 102 runs one ormore simulations on the patient-specific model to facilitate optimizingpatient treatment. Outputs of the simulations may be displayed, forexample, on presentation interface 220 (shown in FIG. 2 ).

For example, prior to implanting a VAD in the patient, simulations maybe run to simulate how the patient would respond to VAD therapy. In thisscenario, the patient model may be built using data from heart failuremonitor 112 and/or CRT device 114 and data from in-clinic measurements,as a VAD has not yet been implanted. These results of these simulationsmay aid the clinician in determining whether or not to implant a VAD,and determining which type of VAD to implant (e.g., LVAD, Bi-VAD, etc.).

After implantation of a VAD, simulations may be run on the patientspecific-model to simulate operation of the VAD. For example, the impactof adjusting pump speed and/or adjusting pulse type may be simulated. Inanother example, the impact of adjusting medications and/or fluid volumemay be simulated.

Further, in some embodiments, modeling and simulation computing device102 analyzes the patient-specific model (e.g., using machine learningand/or other artificial intelligence techniques). The outputs of theseanalyses may be displayed, for example, on presentation interface 220(shown in FIG. 2 ).

For example, as a result of analyzing the patient-specific model,modeling and simulation computing device 102 may recommend changes toVAD parameters (e.g., pump speed) and/or medication to achieve desirablehemodynamic results (e.g., a desired LV pressure profile). In anotherexample, modeling and simulation computing device 102 may track changesin hemodynamic properties over time to identify patterns and/or generatealerts. Tracked hemodynamic properties may include LV/RV systolicfunction, pulmonary and systemic vascular resistance, vessel compliance,fluid/volume status, valve functionality, hematocrit values, etc. In oneexample, modeling and simulation computing device 102 tracks cardiacrecovery (e.g., ventricle function capacity) and optimizes recoveryprotocols on a patient-specific basis (e.g., by recommending medicationdose/timing, pump weaning, pulse types, etc.).

In addition, based on analysis of the patient-specific model, modelingand simulation computing device 102 may generate recommended devicesettings. For example, modeling and simulation computing device 102 maygenerate recommended settings for pump speed, physiologic controlsettings, pulse type, and/or pacemaker settings. In some embodiments,modeling and simulation computing device 102 may control one or more ofremote monitoring data sources 104. For example, modeling and simulationcomputing device 102 may generate and transmit control signals to CRTdevice 114 and/or VAD 116 that instruct CRT device 114 and/or VAD 116 toadjust their settings.

In the embodiments, described herein, the patient-specific model is anonline numerical model that continually updates to match the patient'scurrent condition based on data received from remote monitoring datasources 104 and clinical data sources 106. The more data available toand incorporated into the model, the more accurate the simulations andanalysis performed by modeling and simulation computing device 102.

The patient-specific model may be, for example, a high fidelity lumpedparameter numerical model that simulates a human circulatory system.Further, the model may include features that allow the model to bettermatch actual patients, such as physiologic feedback mechanisms andnon-linear outflow graft dynamics.

For example, in one embodiment, the model includes approximatelyseventy-five parameters that define the behavior of the entirecirculatory system. By varying these parameters, the model can replicatethe behavior of most patients (excluding sever autoregulationdisorders).

Given the variability of VAD patient properties and hemodynamics, insome embodiments, the model is tuned to match a specific patient.Specifically, machine learning and/or other artificial intelligencetechniques are used to systematically vary key model parameters untilthe simulated model replicates actual hemodynamic and pump parameterdata (e.g., ventricle dimensions, PA pressures, etc.) from clinicaldata.

In some embodiments, modeling and simulation computing device 102 alsobuilds a database of generalized models. For example, each generalizedmodel may be built using corresponding clinical study data. Thegenerated database thus includes a plurality of anonymized models. Thesemodels may be searchable, for example, based on generic patientproperties such as gender, race, weight, BMI, cardiac index, INTERMACSclassification, etc. This searchable database allows a user to searchfor an anonymized model based on properties of a patient of interest,and use that anonymized model as a “starting point” for the patientspecific model. Notably, the more similar to the anonymized model to thepatient of interest, the faster the anonymized model will converge to anaccurate patient-specific model. Further, in some embodiments, modelingand simulation computing device 102 receives existing clinical and/ordemographic data associated with the patient, and automatically selectsan anonymized model from the database based on the existing clinicaland/or demographic data.

Using modeling and simulation computing device 102, a user (e.g., aclinician) is able to clearly visualize and understand the mechanismsbehind various patient/device interactions. Further, by runningsimulations, the user can “virtually” experiment with changing pumpspeed, medication, fluid volume, etc., and observe the quantifiedexpected hemodynamic response. Further, using modeling and simulationcomputing device 102, the user can simulate various events (e.g.,dehydration, arrhythmia, exercise, acute hypertension, etc.) to observethe expected outcome and assist in future recognition of such events.

Modeling and simulation computing device 102 also continually updateskey model parameters (e.g., ventricle elastance curves, valveresistance, aortic compliance, etc.) using machine learning and/or otherartificial intelligence techniques to ensure that simulation resultsmatch or closely track clinical data. That is, the patient-specificmodel estimates various patient parameters without a direct physicalmeasurement, which is highly useful for tracking physiological changesover time such as ventricle function, valve leakage, etc.

To demonstrate the efficacy of the systems and methods described herein,an example case study will now be described. Specifically, the followingcase study illustrates how the systems and methods described hereincould be used to aid in the treatment of a dilated cardiomyopathy (DCM)heart failure patient through the continuum of heart failure. Those ofskill will appreciate that similar techniques could be applied to otherheart failure patients as well. This case study is purely fictional, andis intended to demonstrate how the systems and methods described hereincould be implemented.

In this example, assume a patient gradually develops an underlyingelectrophysiological disorder (e.g., left bundle branch block) resultingin deteriorating LV functionality. The patient is initially asymptomaticand unaware of any problems. However, over time, electrical conductionissues worsen, ventricular pumping function becomes compromised, and thepatient occasionally shows minor heart failure (HF) symptoms (i.e.,stage II).

Despite activation of the renin-angiotensin (RAS) feedback system, thepatient is unable to maintain sufficient cardiac output and arterialpressure. Chronically high ventricular pressures cause the LV to becomefurther dilated, further compromising pumping function. The patient isnow symptomatic to the point where they see a cardiologist (i.e., stageIII). The cardiologist performs a full hemodynamic workup, measures anejection fraction of 30%, and identifies the underlying left bundlebranch block disorder.

At this point, the patient is implanted with a pacemaker and a heartfailure monitor (e.g., a PA pressure sensor), and assigned a typical HFmedication regiment. The patient is then registered with an account fora remote monitoring system (which may be the same as or separate frommodeling and simulation computing device 102) and given requiredequipment to enable remote monitoring of the devices. Further, inaccordance with the systems and methods described herein, the clinicianuses modeling and simulation computing device 102 to initialize apatient-specific model for the patient. For example, modeling andsimulation computing device 102 may automatically select initialparameters for the model based on existing clinical and/or demographicdata for the patient.

The pacemaker initially improves cardiac pumping capacity and alleviatesHF systems visible to the patient, returning to stage II. However, priorchronic LV dilation has caused the heart to become mechanicallycompromised and high ventricular filling pressures persist. Over thefollowing months/years, the LV continues to dilate despite successfulpacing. The heart failure monitor tracks PA pressures drifting higher,and these results are passed to the remote monitoring system and arevisible to the clinician. Further, the patient-specific model has beenupdating based on data received from the pacemaker and heart failuremonitor, and now has a matching confidence level to the actual patientof 65%. Modeling and simulation computing device 102 is also reporting,based on analysis of the model, reduced LV systolic function.

The clinician decides to have the patient return to the clinic toreceive a full hemodynamic workup despite generally feeling okay.Results indicate a severely dilated LV and poor general hemodynamics.These in-clinic results are provided to modeling and simulationcomputing device 102, and the matching confidence level increases to75%. The clinician adjusts medication and sends the patient home forremote observation.

Over the next few months, HF symptoms worsen to stage IV, and thepatient is unable to perform daily tasks. The patient-specific modelsuggests very low, and steadily worsening, LV systolic function.Eventually the patient returns to the clinic for another hemodynamicworkup. Results are poor, with an ejection fraction of 20% and lowcardiac index. The patient appears to be a good recovery candidate(younger with only a couple of years in HF), but a poor transplantcandidate (blood type O, weighing over 100 kilograms).

Using modeling and simulation computing device 102, the cliniciansimulates how the patient would respond to VAD therapy (the matchingconfidence level is now 80%). The patient-specific model indicates afavorable hemodynamic response to VAD support and strong RV function.Accordingly, the decision is made to implant a VAD capable ofcommunicating data to modeling and simulation computing device 102.

Within weeks after the VAD implantation, the patient is healthy andwalking around with no HF symptoms (i.e., stage I). The patient-specificmodel, which is now also incorporating an abundance of data from theVAD, has now reached a matching confidence level of 95%. After threemonths, analysis of the patient-specific model by the modeling andsimulation computing device 102 is now indicating improved LV systolicfunction and remodeling. The clinician reviews the logged data andallows the modeling and simulation computing device 102 to instruct theVAD to initiate a recovery protocol, in which the VAD periodicallyreduces pump speed to automatically “train” the LV.

After another two months, analysis of the patient-specific model by themodeling and simulation computing device 102 indicates consistentlystrong LV function, and the weaning algorithms have been implemented toreduce VAD pump speed to relatively low levels such that VAD support isminimal. Using modeling and simulation computing device 102, theclinician simulates removal of the VAD, and the results of thesimulation indicate a high probability of recovery. Thus, the decisionis made to explant the VAD. Two years after explanting the VAD, thepatient shows stable durable recovery. The patient-specific modelmaintained by modeling and simulation computing device 102 is stillactive using available data from the pacemaker and heart failuremonitor.

Accordingly, this case study demonstrates the advantages realized usingthe systems and methods described herein.

FIG. 3 is a block diagram of a method 300 of performing personalizedcardiovascular analyses. Method 300 may be implemented, for example, bymodeling and simulation computing device 102 (shown in FIG. 1 ).

Method 300 includes building 302 a patient-specific model. Method 300further includes storing 304 the patient-specific model in a database.In addition, method 300 includes receiving 306 remote monitoring datafrom at least one remote monitoring data source, and receiving 308clinical data from at least one clinical data source. Method 300 furtherincludes updating 310 the patient-specific model using the remotemonitoring data and the clinical data. Further, method 300 includesperforming 312 at least one simulation on the updated patient-specificmodel and outputting 314 at least one output based on the at least onesimulation.

The systems and methods described herein include building, using amodeling and simulation computing device, a patient-specific model,storing, using the modeling and simulation computing device, thepatient-specific model in a database, receiving, at the modeling andsimulation computing device, remote monitoring data from at least oneremote monitoring data source, and receiving, at the modeling andsimulation computing device, clinical data from at least one clinicaldata source. The systems and methods further include updating, using themodeling and simulation computing device, the patient-specific modelusing the remote monitoring data and the clinical data, performing,using the modeling and simulation computing device, at least onesimulation on the updated patient-specific model, and outputting, fromthe modeling and simulation computing device, at least one output basedon the at least one simulation.

Although certain embodiments of this disclosure have been describedabove with a certain degree of particularity, those skilled in the artcould make numerous alterations to the disclosed embodiments withoutdeparting from the spirit or scope of this disclosure. All directionalreferences (e.g., upper, lower, upward, downward, left, right, leftward,rightward, top, bottom, above, below, vertical, horizontal, clockwise,and counterclockwise) are only used for identification purposes to aidthe reader's understanding of the present disclosure, and do not createlimitations, particularly as to the position, orientation, or use of thedisclosure. Joinder references (e.g., attached, coupled, connected, andthe like) are to be construed broadly and may include intermediatemembers between a connection of elements and relative movement betweenelements. As such, joinder references do not necessarily infer that twoelements are directly connected and in fixed relation to each other. Itis intended that all matter contained in the above description or shownin the accompanying drawings shall be interpreted as illustrative onlyand not limiting. Changes in detail or structure may be made withoutdeparting from the spirit of the disclosure as defined in the appendedclaims.

When introducing elements of the present disclosure or the preferredembodiment(s) thereof, the articles “a”, “an”, “the”, and “said” areintended to mean that there are one or more of the elements. The terms“comprising”, “including”, and “having” are intended to be inclusive andmean that there may be additional elements other than the listedelements.

As various changes could be made in the above constructions withoutdeparting from the scope of the disclosure, it is intended that allmatter contained in the above description or shown in the accompanyingdrawings shall be interpreted as illustrative and not in a limitingsense.

What is claimed is:
 1. A computer-implemented method for performingpersonalized cardiovascular analyses, the method comprising: building,using a modeling and simulation computing device, a patient-specificmodel, wherein the patient-specific model represents a circulatorysystem of a particular patient; storing, using the modeling andsimulation computing device, the patient-specific model in a database;receiving, at the modeling and simulation computing device, remotemonitoring data from at least one remote monitoring data source, theremote monitoring data including at least actual hemodynamic data forthe particular patient; receiving, at the modeling and simulationcomputing device, clinical data from at least one clinical data source;updating, using the modeling and simulation computing device, thepatient-specific model using the remote monitoring data and the clinicaldata, wherein updating the patient-specific model comprises varyingparameters of the patient-specific model until the patient-specificmodel replicates the actual hemodynamic data for the particular patient;performing, using the modeling and simulation computing device, at leastone simulation on the updated patient-specific model that replicates theactual hemodynamic data for the particular patient to simulate operationof a ventricular assist device in the particular patient, wherein the atleast one simulation includes simulating operation of the ventricularassist device in the particular patient while adjusting at least one ofa pulse type, a medication, and a fluid volume; outputting, from themodeling and simulation computing device, at least one output based onthe at least one simulation; analyzing, using the modeling andsimulation computing device, the updated patient-specific model usingmachine learning; generating, using the modeling and simulationcomputing device, based on the analysis, an adjusted operating parameterfor the ventricular assist device; and controlling operation of animplanted device by transmitting a control signal including the adjustedoperating parameter to the implanted device, the control signal causingthe implanted device to adjust operation and begin operating at theadjusted operating parameter.
 2. The method of claim 1, furthercomprising displaying a recommendation including the adjusted operatingparameter.
 3. The method of claim 1, wherein building a patient-specificmodel comprises: generating a database including a plurality ofanonymized patient models; and selecting one of the plurality ofanonymized patient models as the patient-specific model.
 4. A computingdevice for performing personalized cardiovascular analyses, thecomputing device comprising: a memory device; and a processorcommunicatively coupled to the memory device, the processor configuredto: build a patient-specific model, wherein the patient-specific modelrepresents a circulatory system of a particular patient; store thepatient-specific model in the memory device; receive remote monitoringdata from at least one remote monitoring data source, the remotemonitoring data including at least actual hemodynamic data for theparticular patient; receive clinical data from at least one clinicaldata source; update the patient-specific model using the remotemonitoring data and the clinical data, wherein to update thepatient-specific model, the processor is configured to vary parametersof the patient-specific model until the patient-specific modelreplicates the actual hemodynamic data for the particular patient;perform at least one simulation on the updated patient-specific modelthat replicates the actual hemodynamic data for the particular patientto simulate operation of a ventricular assist device in the particularpatient, wherein the at least one simulation includes simulatingoperation of the ventricular assist device in the particular patientwhile adjusting at least one of a pulse type, a medication, and a fluidvolume; output an output based on the at least one simulation; analyzethe updated patient-specific model using machine learning; generate,based on the analysis, an adjusted operating parameter for theventricular assist device; and control operation of an implanted deviceby transmitting a control signal including the adjusted operatingparameter to the implanted device, the control signal causing theimplanted device to adjust operation and begin operating at the adjustedoperating parameter.
 5. The computing device of claim 4, wherein theprocessor is further configured to output a recommendation including theadjusted operating parameter.
 6. The computing device of claim 4,wherein to build a patient-specific model, the processor is configuredto generate a database including a plurality of anonymized patientmodels; and select one of the plurality of anonymized patient models asthe patient-specific model.
 7. Non-transitory computer-readable mediahaving computer-executable instructions thereon, wherein when executedby a processor of a computing device, cause the processor of thecomputing device to: build a patient-specific model, wherein thepatient-specific model represents a circulatory system of a particularpatient; store the patient-specific model in a database; receive remotemonitoring data from at least one remote monitoring data source, theremote monitoring data including at least actual hemodynamic data forthe particular patient; receive clinical data from at least one clinicaldata source; update the patient-specific model using the remotemonitoring data and the clinical data, wherein to update thepatient-specific model, the instructions cause the process to varyparameters of the patient-specific model until the patient-specificmodel replicates the actual hemodynamic data for the particular patient;perform at least one simulation on the updated patient-specific modelthat replicates the actual hemodynamic data for the particular patientto simulate operation of a ventricular assist device in the particularpatient, wherein the at least one simulation includes simulatingoperation of the ventricular assist device in the particular patientwhile adjusting at least one of a pulse type, a medication, and a fluidvolume; output an output based on the at least one simulation; analyzethe updated patient-specific model using machine learning; generate,based on the analysis, an adjusted operating parameter for theventricular assist device; and control operation of an implanted deviceby transmitting a control signal including the adjusted operatingparameter to the implanted device, the control signal causing theimplanted device to adjust operation and begin operating at the adjustedoperating parameter.
 8. The non-transitory computer-readable media ofclaim 7, wherein the instructions further cause the processor to outputa recommendation including the adjusted operating parameter.